The present invention relates generally to a method and apparatus for controlling microelectromechanical systems (MEMS), and more particularly, to a multiagent control system which adaptively selects its organizational structure for controlling movement of an object on a transport assembly. 2. Description of Related Art
Smart matter is defined herein as a physical system or material with arrays of microelectromechanical devices embedded therein for detecting and adjusting to changes in their environment. For example, smart matter can be used to move sheets of paper in a printing machine or maneuver an aircraft by performing tiny adjustments to wing surfaces. Generally, each microelectromechanical device embedded in smart matter contain microscopic sensors, actuators, and controllers. A characteristic of smart matter is that the physical system consists large numbers (possibly thousands) of microelectromechanical devices. These devices work together to deliver a desired higher level function (e.g., moving a piece of paper from one location to another or flying a plane).
Programs for controlling smart matter do not always adequately achieve the desired higher level function of issuing command to compensate for detected changes in a physical system because of the significant number of devices that operate in parallel to control it. That is, there exists a number of factors which make the computational task of a control program for smart matter difficult. One factor which may be cause control programs to be computationally intense is caused by the high redundancy of sensors and actuators in the physical material. In order for smart matter systems to exhibit the enhanced reliability and robustness over conventional systems, smart matter systems contain many more devices than necessary to achieve a desired performance. Failure or improper function of some elements, even a significant fraction, is compensated by the actions of the redundant components. Moreover, the ability of smart matter systems to tolerate component failure can be used beneficially to lower the fabrication cost of the components.
One approach for controlling smart matter is to rely on a single global processor coupled with rapid access to the full state of the system and detailed knowledge of system behavior. This method, however, has been proved ineffective because of the large number of devices embedded in smart matter. Another approach for controlling smart matter is through the use of a collection of autonomous computational agents (or elements) that use sensor information to determine appropriate actuator forces. Using multiple computational agents (i.e., distributed control) instead of a single global processor (i.e., central control) may prove more effective because each computational agent is only concerned with a limited aspect of the overall control problem. In some multi-agent systems, individual agents are associated with a specific sensor or actuator embedded in the physical system. This method for controlling smart matter defines a community of computational agents which, in their interactions, strategies, and competition for resources, resemble natural ecosystems. Furthermore, by distributing control among computational agents, the system as a whole is better able to adapt to environmental changes or disturbances because the system can compensate for new circumstances by simply changing the relationship of the agents.
Although multi-agent systems have been used to solve distributed control problems, they have been limited to systems which are physically large. For example, multi-agent systems have been used in distributed traffic control, flexible manufacturing, robotic system design, and self-assembly structures. It is difficult to use multi-agent systems to control smart matter because of the tight coupling between computational agents and their embedded physical space. Furthermore, controlling smart matter using traditional multi-agent systems is difficult because of mechanical interactions that decrease in strength with the physical distance between them. This makes the computational problem difficult because interactions between computational agents cannot be ignored.
When the values of system parameters are known, a global model which accurately defines system behavior can be constructed. In practice, however, sufficiently detailed information of the system is seldom available to construct such a global model of system behavior. Depending on the system, detailed information may not be readily available because behavioral changes due to environmental contamination of, or damage to, the system's actuators and/or sensors. It would, therefore, be desirable to have a distributed multi-agent system that can automatically adjusts its organization to continually improve performance in the face of such behavioral changes.
There exists, therefore, a need to provide a multi-agent system for controlling smart matter that solves these as well as other control problems. In addition, it would be desirable to provide a distributed control system for smart matter that is able to adapt to a physical system which has differing specifications because of manufacturing tolerances or defects. More specifically, it would be desirable to provide a controller for smart matter that robustly coordinates a physically distributed real-time response with many devices in the face of failures, delays, changing environmental conditions, and incomplete models of system behavior.